
import json
import os
import unittest

from transformers import BatchEncoding, MvpTokenizer, MvpTokenizerFast
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin, filter_roberta_detectors

@require_tokenizers
class 							_UpperCamelCase					(       _A  ,							unittest.TestCase	):






			'''simple docstring'''

			__UpperCamelCase   :    List[Any]          =			MvpTokenizer
			__UpperCamelCase   :    Dict          =			MvpTokenizerFast
			__UpperCamelCase   :    int          =			True
			__UpperCamelCase   :    str          =			filter_roberta_detectors

			def    lowerCAmelCase__						(							self			:							List[Any]   ):
							super().setUp()
							UpperCamelCase_:				Any						=    [
							    """l""",
							    """o""",
							    """w""",
							    """e""",
							    """r""",
							    """s""",
							    """t""",
							    """i""",
							    """d""",
							    """n""",
							    """\u0120""",
							    """\u0120l""",
							    """\u0120n""",
							    """\u0120lo""",
							    """\u0120low""",
							    """er""",
							    """\u0120lowest""",
							    """\u0120newer""",
							    """\u0120wider""",
							    """<unk>""",
							]
							UpperCamelCase_:				Tuple						=    dict(zip(snake_case_    ,						range(len(snake_case_   )   )   )   )
							UpperCamelCase_:				Optional[Any]						=    ["""#version: 0.2""", """\u0120 l""", """\u0120l o""", """\u0120lo w""", """e r""", """"""]
							UpperCamelCase_:				List[Any]						=    {"""unk_token""": """<unk>"""}

							UpperCamelCase_:				List[Any]						=    os.path.join(self.tmpdirname    ,						VOCAB_FILES_NAMES["""vocab_file"""]   )
							UpperCamelCase_:				List[str]						=    os.path.join(self.tmpdirname    ,						VOCAB_FILES_NAMES["""merges_file"""]   )
							with open(self.vocab_file    ,						"""w"""    ,						encoding="""utf-8"""   ) as fp:
											fp.write(json.dumps(snake_case_   ) + """\n"""   )
							with open(self.merges_file    ,						"""w"""    ,						encoding="""utf-8"""   ) as fp:
											fp.write("""\n""".join(snake_case_   )   )

			def    lowerCAmelCase__						(							self			:							Union[str, Any]    ,						**snake_case_			:							Optional[int]   ):
							kwargs.update(self.special_tokens_map   )
							return self.tokenizer_class.from_pretrained(self.tmpdirname    ,						**snake_case_   )

			def    lowerCAmelCase__						(							self			:							Union[str, Any]    ,						**snake_case_			:							Optional[int]   ):
							kwargs.update(self.special_tokens_map   )
							return self.rust_tokenizer_class.from_pretrained(self.tmpdirname    ,						**snake_case_   )

			def    lowerCAmelCase__						(							self			:							str    ,						snake_case_			:							List[Any]   ):
							return "lower newer", "lower newer"

			@cached_property
			def    lowerCAmelCase__						(							self			:							str   ):
							return MvpTokenizer.from_pretrained("""RUCAIBox/mvp"""   )

			@cached_property
			def    lowerCAmelCase__						(							self			:							Tuple   ):
							return MvpTokenizerFast.from_pretrained("""RUCAIBox/mvp"""   )

			@require_torch
			def    lowerCAmelCase__						(							self			:							Optional[Any]   ):
							UpperCamelCase_:				int						=    ["""A long paragraph for summarization.""", """Another paragraph for summarization."""]
							UpperCamelCase_:				Dict						=    [0, 250, 251, 1_7818, 13, 3_9186, 1938, 4, 2]

							for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
											UpperCamelCase_:				int						=    tokenizer(snake_case_    ,						max_length=len(snake_case_   )    ,						padding=snake_case_    ,						return_tensors="""pt"""   )
											self.assertIsInstance(snake_case_    ,						snake_case_   )

											self.assertEqual((2, 9)    ,						batch.input_ids.shape   )
											self.assertEqual((2, 9)    ,						batch.attention_mask.shape   )
											UpperCamelCase_:				List[Any]						=    batch.input_ids.tolist()[0]
											self.assertListEqual(snake_case_    ,						snake_case_   )
											# Test that special tokens are reset

			@require_torch
			def    lowerCAmelCase__						(							self			:							Union[str, Any]   ):
							UpperCamelCase_:				int						=    ["""A long paragraph for summarization.""", """Another paragraph for summarization."""]
							for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
											UpperCamelCase_:				List[Any]						=    tokenizer(snake_case_    ,						padding=snake_case_    ,						return_tensors="""pt"""   )
											# check if input_ids are returned and no labels
											self.assertIn("""input_ids"""    ,						snake_case_   )
											self.assertIn("""attention_mask"""    ,						snake_case_   )
											self.assertNotIn("""labels"""    ,						snake_case_   )
											self.assertNotIn("""decoder_attention_mask"""    ,						snake_case_   )

			@require_torch
			def    lowerCAmelCase__						(							self			:							List[str]   ):
							UpperCamelCase_:				Dict						=    [
							    """Summary of the text.""",
							    """Another summary.""",
							]
							for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
											UpperCamelCase_:				Any						=    tokenizer(text_target=snake_case_    ,						max_length=32    ,						padding="""max_length"""    ,						return_tensors="""pt"""   )
											self.assertEqual(32    ,						targets["""input_ids"""].shape[1]   )

			@require_torch
			def    lowerCAmelCase__						(							self			:							int   ):
							for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
											UpperCamelCase_:				str						=    tokenizer(
											    ["""I am a small frog""" * 1024, """I am a small frog"""]    ,						padding=snake_case_    ,						truncation=snake_case_    ,						return_tensors="""pt"""   )
											self.assertIsInstance(snake_case_    ,						snake_case_   )
											self.assertEqual(batch.input_ids.shape    ,						(2, 1024)   )

			@require_torch
			def    lowerCAmelCase__						(							self			:							Tuple   ):
							UpperCamelCase_:				Any						=    ["""A long paragraph for summarization."""]
							UpperCamelCase_:				Tuple						=    [
							    """Summary of the text.""",
							]
							for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
											UpperCamelCase_:				Dict						=    tokenizer(snake_case_    ,						text_target=snake_case_    ,						return_tensors="""pt"""   )
											UpperCamelCase_:				List[str]						=    inputs["""input_ids"""]
											UpperCamelCase_:				str						=    inputs["""labels"""]
											self.assertTrue((input_ids[:, 0] == tokenizer.bos_token_id).all().item()   )
											self.assertTrue((labels[:, 0] == tokenizer.bos_token_id).all().item()   )
											self.assertTrue((input_ids[:, -1] == tokenizer.eos_token_id).all().item()   )
											self.assertTrue((labels[:, -1] == tokenizer.eos_token_id).all().item()   )

			def    lowerCAmelCase__						(							self			:							str   ):
							pass




			def    lowerCAmelCase__						(							self			:							str   ):
							for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
											with self.subTest(f'''{tokenizer.__class__.__name__} ({pretrained_name})'''   ):
															UpperCamelCase_:				Tuple						=    self.rust_tokenizer_class.from_pretrained(snake_case_    ,						**snake_case_   )
															UpperCamelCase_:				Tuple						=    self.tokenizer_class.from_pretrained(snake_case_    ,						**snake_case_   )
															UpperCamelCase_:				Union[str, Any]						=    """A, <mask> AllenNLP sentence."""
															UpperCamelCase_:				List[Any]						=    tokenizer_r.encode_plus(snake_case_    ,						add_special_tokens=snake_case_    ,						return_token_type_ids=snake_case_   )
															UpperCamelCase_:				Union[str, Any]						=    tokenizer_p.encode_plus(snake_case_    ,						add_special_tokens=snake_case_    ,						return_token_type_ids=snake_case_   )

															# token_type_ids should put 0 everywhere
															self.assertEqual(sum(tokens_r["""token_type_ids"""]   )    ,						sum(tokens_p["""token_type_ids"""]   )   )

															# attention_mask should put 1 everywhere, so sum over length should be 1
															self.assertEqual(
															    sum(tokens_r["""attention_mask"""]   ) / len(tokens_r["""attention_mask"""]   )    ,						sum(tokens_p["""attention_mask"""]   ) / len(tokens_p["""attention_mask"""]   )    ,						)

															UpperCamelCase_:				List[str]						=    tokenizer_r.convert_ids_to_tokens(tokens_r["""input_ids"""]   )
															UpperCamelCase_:				Optional[Any]						=    tokenizer_p.convert_ids_to_tokens(tokens_p["""input_ids"""]   )

															# Rust correctly handles the space before the mask while python doesnt
															self.assertSequenceEqual(tokens_p["""input_ids"""]    ,						[0, 250, 6, 5_0264, 3823, 487, 2_1992, 3645, 4, 2]   )
															self.assertSequenceEqual(tokens_r["""input_ids"""]    ,						[0, 250, 6, 5_0264, 3823, 487, 2_1992, 3645, 4, 2]   )

															self.assertSequenceEqual(
															    snake_case_    ,						["""<s>""", """A""", """,""", """<mask>""", """ĠAllen""", """N""", """LP""", """Ġsentence""", """.""", """</s>"""]   )
															self.assertSequenceEqual(
															    snake_case_    ,						["""<s>""", """A""", """,""", """<mask>""", """ĠAllen""", """N""", """LP""", """Ġsentence""", """.""", """</s>"""]   )








from __future__ import annotations







def 							A__       (    lowerCamelCase    )	->						bool:
				UpperCamelCase_:				Optional[int]						=    len(lowerCamelCase    )
				# We need to create solution object to save path.
				UpperCamelCase_:				List[str]						=    [[0 for _ in range(lowerCamelCase    )] for _ in range(lowerCamelCase    )]
				UpperCamelCase_:				Dict						=    run_maze(lowerCamelCase  ,     0  ,     0  ,     lowerCamelCase    )
				if solved:
								print("""\n""".join(str(lowerCamelCase    ) for row in solutions    )    )
				else:
								print("""No solution exists!"""    )
				return solved







def 							A__       (    lowerCamelCase  ,     lowerCamelCase  ,     lowerCamelCase  ,     lowerCamelCase    )	->						bool:
				UpperCamelCase_:				Dict						=    len(lowerCamelCase    )
				# Final check point.
				if i == j == (size - 1):
								UpperCamelCase_:				Union[str, Any]						=    1
								return True

				UpperCamelCase_:				List[str]						=    (not i < 0) and (not j < 0)  # Check lower bounds
				UpperCamelCase_:				List[str]						=    (i < size) and (j < size)  # Check upper bounds

				if lower_flag and upper_flag:
								# check for already visited and block points.
								UpperCamelCase_:				Dict						=    (not solutions[i][j]) and (not maze[i][j])
								if block_flag:
												# check visited
												UpperCamelCase_:				Optional[Any]						=    1

												# check for directions
												if (
												    run_maze(lowerCamelCase  ,     i + 1  ,     lowerCamelCase  ,     lowerCamelCase    )
												    or run_maze(lowerCamelCase  ,     lowerCamelCase  ,     j + 1  ,     lowerCamelCase    )
												    or run_maze(lowerCamelCase  ,     i - 1  ,     lowerCamelCase  ,     lowerCamelCase    )
												    or run_maze(lowerCamelCase  ,     lowerCamelCase  ,     j - 1  ,     lowerCamelCase    )
												):
																return True

												UpperCamelCase_:				Optional[Any]						=    0
												return False
				return False


if __name__ == "__main__":
							import doctest

							doctest.testmod()






